Rutgers University New Brunswick

NSF Awards · FY 2024 · 2024-11

The broader impact of this I-Corps project is the development of carbon-based electronic materials with graphite-like structure derived from consumer plastic. Graphite is listed as a critical material by the U.S. government as it plays a key role in various energy technologies. However, both the mining and synthetic production of graphite consume significant amounts of energy, and large-scale mining is confined to relatively few locations. Synthetic graphite manufacturing is a major source of graphite used for energy applications. Parts of the synthesis occur at extremely high temperature (>2000 oC). Additionally, the raw material for synthetic graphite production is derived from a residue produced during crude oil distillation, which is also a complex and expensive process. The new materials are promising for use as a composite together with silicon to improve the capacity of battery anodes in electric vehicle applications. The solution is also promising for use as a coating to improve the conductivity of electrodes in electronic devices. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of a technology to efficiently transform the consumer plastic, polypropylene, into carbon nanomaterials with graphitic structure, called carbon dots. The purified carbon dots are less than 5 nm in diameter and, through additional processing, can be assembled into larger crystallites (> 50 nm in size) as indicated from powder X-ray diffraction and electron microscopy. The X-ray diffraction patterns indicate a graphite-like structure, which makes it a potential substitute for graphite in various industrial applications with a higher surface-to-volume ratio. In addition, this material has been shown to be electrically conductive with no metal included inside. Understanding the assembling behavior of the carbon dots may lead to a new approach to producing microscale graphite from consumer plastic. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-11

One-dimensional nanostructures, such as nanorods, nanowires, and their assemblies, have numerous applications in surface modification for advanced mechanical, optical, or bioactive functions. However, established techniques for manufacturing these nanomaterials and their deposition as coatings are limited to small scales and often flat surfaces due to cost, throughput, or complexity. This grant supports fundamental research to provide needed knowledge for manufacturing multifunctional nanomaterial and composite conformal coatings at scale. The development of new manufacturing processes based on electrostatically-induced sprays of waterborne gelling polymers enables the mass production of nanowire coatings on versatile surfaces with complex shapes and three-dimensional features, which can be widely used in energy storage, tissue engineering, smart textiles, and water/air filtration. Simultaneously, the gained understanding can be translated into additional materials systems and new applications, which benefit the U.S. economy and prosperity. The broader impacts activities contribute to the research education pipeline and workforce development to secure U.S. global leadership in materials manufacturing by training students and research fellows at various levels and engaging with the broader scientific community and the general public through outreach. Electrospray deposition has shown great promise for manufacturing polymeric nanostructures. Typical morphologies of electrospray deposits are hierarchical assemblies of nanoparticles and overlaid in-plane nanofiber mats if droplet breakup is suppressed (i.e., electrospinning). The goal of this project is to enable electrospray deposition of aqueous methylcellulose solutions to produce polymer and polymer-nanoparticle composite nanowires with well-controlled dimensions on a drop-by-drop basis. This research fills the knowledge gap on the interplay between self-assembly and morphology development in multiphase droplets generated in the sprays. The research team integrates advanced experimental techniques, including X-ray characterization, microscopy, and laser strobe imaging, with mesoscale multiphysical modeling to determine the physical mechanisms of dropwise nanowire formation and deposition. New dissipative particle dynamics simulations elucidate the self-assembly dynamics of methylcellulose in nonequilibrium conditions and predict the electrohydrodynamic deformation of composite droplets. The effects of particle-polymer interaction, particle entropy, and spatial confinement are explored for controlling filler distribution and functional properties of the composite wires. The team extends the study to other materials to demonstrate the generality of nanowire formation in electrospray by rapid, homogeneous viscosity transition in the droplets. Through collaborative experimental and modeling efforts, this research advances the understanding of the electrospray of complex fluids and provides a foundation for scalable manufacturing of nanowire composites and their superstructures. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-11

Cuscuta (common name: dodder; Convolvulaceae) is a diverse genus of parasitic plants that causes major crop losses across the US and the globe. While dodder that are major pests attack a wide range of host species, many show host preferences, and dodder growth varies substantially across hosts. This apparent host preference suggests some dodder genotypes and populations are adapted to a particular set of hosts. Understanding the genetic mechanisms of these host preferences could help farmers in their battle against dodder. The central biological question of this project is: how do agriculturally relevant Cuscuta species successfully parasitize a wide range of hosts? The research team will study variation in DNA sequence and gene expression across diverse dodder populations across diverse host species to answer this question. Additionally, the team will pursue agronomic research, extension, and outreach as a part of broader impacts. In particular, the team will identify dodder-resistant cultivars of blueberries and determine the role of over-wintering of dodder in driving subsequent year infestations. Additionally, lessons in plant biology and research projects will be developed with blind and visually impaired and deaf and hard of hearing students. The research team will address the central question, how do agriculturally relevant Cuscuta species successfully parasitize a wide range of hosts, with three main aims: 1) profiling the diversity of trans-species miRNAs across Cuscuta populations and identifying their host mRNA targets, 2) resequencing Cuscuta genomes to characterize population genetic processes affecting miRNA diversity and host-specificity, and 3) determining how genetic variation in Cuscuta response to hosts is driven by gene expression and associated with success of attachments to hosts. The study will cover the two most common species of Cuscuta in the study region of the northeast US, C. campestris and C. gronovii. Small RNA sequencing will be used combined with host transcriptome assemblies to study the diversity of trans-species miRNAs and their targets across the region. Inference of the importance of miRNA variation will be tested using mutants in host mRNA targets. Population genetic inference will be applied to understand what evolutionary processes have shaped diversity in host-responsive genetic loci. Microscopy and RNAseq will be used to study how genetic variation in Cuscuta interacts with different host species to determine attachment success and gene expression. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

The goal of this project is to develop innovative computational methods that integrate classical and quantum algorithmic tools within the fields of statistics and operations research. The project focuses on applications involving models such as stochastic differential equations, and areas such as machine learning, and data analytics, which arise in various applied engineering and scientific disciplines. Leveraging the diverse expertise of the research team in classical and quantum algorithms, statistics, and operations research, the project will develop quantum-enhanced algorithms for decision-making under uncertainty in both single-stage and multi-stage settings, as well as quantum-accelerated multilevel Monte Carlo methods. These methods will enable, by means of substantially faster algorithms, significant advances in the design of efficient Bayesian inference and machine learning procedures. They will also benefit practitioners across various scientific domains in the physical and social sciences. The project's educational and outreach efforts include curriculum development, diversity initiatives, workshops, and partnerships with local schools. These efforts will broaden the participation of the computing community both in terms of the use of novel quantum methods but also in their application to a wide range of applications. The research plan builds on recent advancements in both quantum and classical algorithms, including contributions from the team members. By developing new quantum Monte Carlo estimators and leveraging advances in parallel randomized multilevel Monte Carlo methods, the team will systematically explore quadratic speed-ups through variable time quantum algorithms and quantum-inside-quantum Monte Carlo strategies. Specific objectives include developing quantum Monte Carlo strategies for solving Markov decision problems with a guaranteed query complexity comparable to evaluating a policy (not necessarily optimal). Another objective is the analysis of stochastic optimization problems with a zero-order oracle, achieving quadratic speed-ups compared to classical approaches. The researchers will further explore quantum accelerated algorithms for computing expectations under a wide range of equilibrium/Boltzmann distributions. Moreover, the investigators will establish upper and lower bounds that confirm the optimality of the quantum accelerated algorithms. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

The extended Berkeley Packet Filter (eBPF) adds dynamism to the Linux operating system kernel by allowing user-space programs to be executed in the kernel upon specific events. Such dynamism enables the development of novel applications for networking, system security, and performance profiling. eBPF-based applications have seen widespread practical deployment in the industry. To ensure the safety and security of executing user-developed programs in the operating system context, the Linux kernel implements algorithms for static program analysis through abstract interpretation, encapsulated into an in-kernel component called the eBPF verifier. Given that Linux is a widely used operating system, a bug in the eBPF verifier can compromise the safety and security of all devices using it. This project's novelty is in the design of new automated techniques to prove the correctness of the abstract interpretation algorithms in the eBPF verifier. This project's key impact is the development of a provably sound eBPF verifier, with the potential to make widely deployed eBPF software and the Linux kernel much more secure and reliable. This project will educate and train graduate and undergraduate students on formal methods and networking, through the development of curricular materials and research projects. This project advances formal verification of low-level systems and networking software by (i) developing and enhancing a software pipeline to automatically check the soundness of abstract interpretation operators in the Linux kernel directly from C source code; (ii) introducing techniques to modularize and parallelize verification tasks to improve the feasibility of verifying every kernel commit; (iii) producing high-quality bug reports through differential synthesis of programs that demonstrate mismatches between concrete execution and abstract interpretation; and (iv) pursuing collaborations with Linux kernel developers to increase the likelihood of deploying formal checking in the Linux kernel’s continuous integration (CI) infrastructure. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

Wireless communication services and associated applications rely on the use of radio frequency (RF) spectrum resources for their operation. Due to the rapid growth in the use of these services, spectrum management agencies and wireless service providers need to migrate from current spectrum use practices to more dynamic spectrum assignment and sharing mechanisms. This project addresses these challenges by focusing on the design and validation of a distributed and data-driven next-generation architecture for dynamic spectrum management among decentralized and heterogeneous wireless systems. Aspects of the distributed spectrum architecture are expected to influence future technical standards. The outcomes of the project will be made available to the wireless/networking industry through mechanisms such as the bi-annual WINLAB industrial advisory meeting. The project integrates activities related to the use and design of spectrum deconfliction protocols and the execution of measurements to design and use spectrum consumption models into the annual WINLAB summer internship program which involves about 30 to 40 undergraduate students each year. Distributed dynamic spectrum management aims to overcome the limitations of centralized control such as limited scalability and single point of failure, while still achieving high levels of spectrum efficiency. The distributed data-driven spectrum management (D3SM) architecture that serves as the baseline for this project uses an Internet-based control plane that facilitates the operation of dynamic spectrum sharing algorithms between peer networks. This control plane for spectrum coordination supports the exchange of and processing of fine-grained meta-data about the local wireless environment in the form of standardized radio frequency spectrum usage descriptors known as “spectrum consumption models (SCMs)” which have recently been standardized. Such spectrum usage data can be used to realize a flexible range of distributed algorithms and dynamic interactions for spectrum coordination. It is noted that a suitably designed distributed spectrum management framework can also accommodate some level of hierarchically organized centralized coordination where appropriate. The project is based on a multi-stage evaluation methodology that starts with architectural design of D3SM with the required protocols and algorithms, followed by simulation and indoor testbed emulation of a number of use case scenarios including spectrum sharing between cellular operators, coexistence of WiFi and 5G, and interference management for passive wireless devices such as those used for weather forecasting and radio astronomy. These studies are expected to lead to an experimentally validated set of protocols and algorithms for distributed and partially centralized spectrum management methods. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

Artificial intelligence (AI) and machine learning (ML) has been widely and successfully used in many fields including transportation, autonomous driving, chip design, etc. Considering the profound impact of AI as a potent force of transformation across various societal domains, AI ethics has garnered significant scrutiny. AI systems trained on biased data can perpetuate or amplify negative biases, with profound implications for areas like criminal justice, hiring, and lending, where biased AI could lead to unfair or discriminatory outcomes. Designing an ethical AI system has significant social and political value. As AI models grow, the demand for cyberinfrastructure (CI) support becomes substantial. Much research has focused on designing high-performance computing (HPC) infrastructures to accelerate AI/ML. However, support from CI for ethical AI is lacking, primarily due to distinctive constraints introduced by ethical considerations. Notably, such ethical constraints or objectives integrated with AI algorithms can slow down the inference and training processes. Conversely, without consideration of ethical AI, traditional CI technologies such as quantization and approximation might compromise AI ethics, even if they expedite the computation. This project will establish interactive and integrated training for building high-performance ethical AI with three interdisciplinary training programs across philosophy, ethical AI, and HPC. These include nine training modules and activities for sustainability and fostering community. The goal is to fill the gap between CI and ethical AI and AI ethics and train both CI contributors and CI users to build high-performance ethical AI. The training programs include: 1) Philosophical AI ethics training for CI contributors and ethical AI designers; 2) Ethical AI training for CI contributors; 3) CI software and hardware technologies training for ethical AI designers. Moreover, several hands-on projects are proposed to deepen trainees’ understanding of those programs, including hardware acceleration for machine learning models, ethical AI implementation, etc. The long-term goal is to boost the adoption of new "Computing+AI+Ethics" to multidisciplinary students and researchers from different STEM domains. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

In our fast-paced and increasingly online world making informed decisions is both more important and harder than ever. In response, this project will create a new research direction called informed group decision making. This new research area extends current models and mechanisms of group decision making by explicitly accounting for the role that information has on agents' final decisions. The final goal is to develop new models and methods that can be used to incentivize individuals to ensure group decisions achieve a desired outcome. This research consists of three dimensions for foundational research and one direction for bridging theory and practice. Dimension 1: Representation aims to develop novel models for combining agents' subjective and objective preferences, information, and responses to queries. Dimension 2: Aggregation aims to introduce novel criteria for informed group decision making, and design novel mechanisms to achieve them. Dimension 3: Incentives aims to address agents' incentives in informed group decision making by proposing novel equilibrium concepts, conducting analysis of agents' behavior, and designing novel incentive-aware mechanisms. To bridge theory and practice, the models, algorithms, and mechanisms developed in this project will be deployed, validated, and refined at the open-source Online Preference Reporting and Aggregation (OPRA) system via various educational and outreach activities. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

Billions of people rely on software to prepare written communication. These software are now equipped with chatbot-style interfaces that automate tasks of text production without adequate safeguards. These safeguards aim to sustain the wide-ranging proficiencies that go into effective communication or maintain the quality and integrity of research and written work. This project will lay the groundwork for new paradigms of responsible design, development, and deployment of artificial intelligence (AI)-powered writing tools with a focus on the “college writing” domain at a large top-tier university. The project’s interdisciplinary collaborations will connect instructors and learners of college writing, researchers in natural language processing (NLP), and domain experts from the humanities, to set an agenda for research in generative AI with the potential to enhance rich practices of research, writing, and communication in and beyond higher education. The design-oriented approach supports and empowers community-led participation in technology design and keeps the public interest firmly in view. The project combines outreach to instructors and students of "writing" at Rutgers University (a broad category based on core writing requirements) and design labs that put insights gained from this outreach in dialogue with the goals and methods of AI research. Outreach efforts will clarify: 1) the learning objectives that writing communities aspire to and achieve; 2) the strengths, weaknesses, values, and aspirations that they bring to these endeavors; 3) the current capabilities that writing tools already offer, including observed strengths and limitations; and 4) a technically specific and socially rich account of the needs and expectations for building community-centered paradigms for responsible AI. Design labs will enable the documentation of meaningful ways that these communities can leverage existing and near-term AI tools in their writing practices–with the right kinds of preparation and facilitation—and will lead to new perspectives on AI infrastructures and architectures that could significantly streamline future efforts but might require longer-term research and investment. The vision for the outcome of this planning project includes: 1) clear and accessible articulations of the research methods and challenges for design-oriented AI as it applies to writing; 2) models for the equitable and sustainable trajectory of the technology in the college classroom; and 3) resources that make it easier to pursue research in this area, such as software infrastructure, preliminary results, hypotheses, open questions, and an inclusive, collaborative research network. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

Immersive multimedia systems, such as volumetric video (VV), virtual reality (VR), and augmented reality (AR) systems, have become popular recently and attracted a broad range of user applications. While presenting a fully interactive and engaging user experience, the immersive multimedia poses unique security challenges associated with its fine-grained three-dimensional (3D) content. In particular, the 3D content (e.g., 3D human face) may be exploited by adversaries to issue biometric-based attacks (e.g., spoofing attacks against face authentication), causing significant security and societal concerns. This project aims to investigate and address such security challenges in volumetric videos by developing (1) an advanced face spoofing attack leveraging real-time environment lighting estimation and generation; and (2) an effective countermeasure injecting protective perturbations to the 3D content, which invalidates the spoofing attack while maintaining the original quality of experience. As a pilot study on the security implications of immersive multimedia, the proposed project will benefit the larger-scale adoption of VR and AR technologies in many fields of studies involving sensitive data and computations, such as teleconferencing, remote education, and healthcare. The project consists of three research thrusts. First, it demonstrates that the state-of-the-art face authentication systems, even equipped with advanced liveness detection mechanisms, can be effectively compromised by the 3D face models leveraging the proposed lighting estimation and generation approach. Second, it proposes a real-time perturbation generation mechanism to obfuscate and protect the sensitive 3D content from being exploited for spoofing attacks. Third, it develops an evaluation framework in both lab and community settings to verify the effectiveness of the proposed security approaches and expand the scope of the project to the broader communities. Overall, the project aims to fill the critical security gap in the popular immersive multimedia systems and pave the way toward larger-scale and user-facing deployments. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

Fair division deals with the distribution of resources and tasks among different parties, e.g., individuals, firms, nations, or autonomous agents, with the goal of achieving fairness and economic efficiency. Fairness has increasingly become crucial in distributing precious and scarce medical equipment, and its absence has exacerbated healthcare issues during the COVID-19 global pandemic. A wide variety of real-world applications such as scheduling, dispute resolution, healthcare management, and refugee settlement assume complete knowledge about allocation decisions, which gives rise to negative computational and impossibility results. The existing approaches to mitigate these challenges, in turn, impose a high cost on transparency. The broad goal of this project is to provide theoretical and algorithmic solutions for fair allocation of indivisible items in practical, large-scale settings, as a broad contribution to the grand scheme of artificial intelligence (AI) and economics for social good. This research will offer a novel and promising perspective for developing practical and transparent fair solutions while providing a systematic investigation on the perceived fairness of allocation mechanisms that are applicable to societies at large. This project will integrate and develop algorithmic solutions for transparent fair division in a publicly available software system with the goal of extending its reach--and in general promoting fairness and transparency--to a broad national and international audience. This project will develop a new framework for achieving fairness and efficiency in the allocation of indivisible resources with minimum cost on transparency. Specifically, it will make progress in four interconnected dimensions: 1) Tradeoffs between transparency, fairness, and efficiency, that aim at analyzing the compatibility of the properties and devising algorithmic solutions when allocating indivisible items, 2) Strategic aspects of fair division, that investigates agents' behavior and strategies under transparency requirements, 3) Domain restriction, that focuses on developing tractable solutions by circumventing the impossibility results in achieving compatible solutions, and 4) Bads and mixtures, that extend the transparency and fairness framework to include desirable (goods) and undesirable items (bads). Furthermore, this research plans to close the current gap between theoretical foundations of fairness and the perception of fairness through a series of comprehensive empirical evaluations. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

The Integrated Machine-learning for PRotein Structures at Scale (IMPRESS) project aims to harness the power of artificial intelligence (AI) and high-performance computing (HPC) to revolutionize the way we design and validate proteins tailored for specific purposes. Creating novel proteins can potentially transform numerous aspects of human life. IMPRESS will address fundamental challenges in AI-driven protein design, including determining optimal neural architectures, efficient training of foundational models, and integrating diverse data sources such as experimental and simulation data. This will enhance the accuracy and efficiency of protein design and provide the necessary computing capabilities to pave the way for future research and development. The project will provide valuable training opportunities for students and early-career researchers. By enabling the effective creation of high-quality tailored proteins, the project can potentially deliver many tangible benefits to society. Artificial intelligence and computing advances have set the stage for designing novel proteins tailored for specific purposes. However, the space of possible protein sequences and structures is astronomically large, even for modestly long proteins; thus, obtaining high convergence between generated and predicted structures requires significant computational resources in sampling. Coupling AI systems with traditional HPC simulations promises significant scientific acceleration, defined as the number of high-quality structures for a given computational cost. The Integrated Machine Learning for Protein Structures at Scale (IMPRESS) project will enhance our ability to tailor proteins by designing and implementing advanced systems that support the online coupling of AI with HPC tasks. Specifically, this project will accelerate the evaluation of possible protein sequences over “vanilla” approaches that do not leverage the online coupling of AI and HPC capabilities. The integrated AI-HPC infrastructure and methodology will provide the ability to “evaluate as you go” the effectiveness of models and evolve the specific set of simulations used to generate data and train models. IMPRESS will also enable novel modes and methods in online coupling and concurrent execution of AI and HPC on the NAIRR platform. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

The ability to learn language is a fundamental part of being human, but researchers do not yet understand the mechanisms that underlie this ability. This research focuses on two aspects of this mystery: how humans learn the words of their language (morphology), and how they acquire the systematic variation in the pronunciation of these words (phonology). For example, English speakers can recognize that the words "atomize" (AE-tuh-mahyz) and "atomic" (uh-TOM-ik) are both based on the root "atom," even though the sounds in that root are pronounced differently in the two words. An influential hypothesis is that people store mental representations of the pronunciations of words that abstract away from some finer aspects of their pronunciation. This research aims to make progress towards answering the question of how these mental representations are formed, and how speakers learn the language-specific patterns governing the systematic variation in the pronunciation of related words. The proposed research informs hypotheses regarding the organization of mental representations of words and phonological patterns in children, provides interpretable algorithms and software which can be used for analysis and processing of low-resource languages, and provides insights into sequential processing and learning more generally, which has applications in other fields such as bioinformatics and planning and control systems in robotics. The leading idea guiding this research is that linguistic generalizations are not arbitrary, and are in fact guided by computational and structural restrictions related to memory and perception. It is well-known that such restrictions can be expressed with particular (called subregular) forms of logic and automata. This research applies a modular, interpretable, and small-data approach to tackle the problem of simultaneously learning the mental representations of words and their morphological and phonological patterning. This research is based on foundational results connecting the computational complexity of morphological and phonological patterns to learning procedures which are specific to subregular logics and automata. Computational analysis of the principles that guide morpho-phonological analysis are combined with algorithms based on subregular properties of morphological and phonological patterning, which are (1) suitably generalized to a variety of morpho-phonological representations and (2) made robust to optionality, variation, and exceptions. The goal is to better understand, qualitatively and computationally, the mechanisms which underlie the human capacities for both constructing mental representations of words and learning the morpho-phonological patterns governing them. Research activities also provide the resulting algorithms as open-source software and evaluate them empirically and quantitatively, with a focus on low-resource languages. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

This grant supports research that contributes new knowledge related to a novel manufacturing process called Form-Fuse. This process creates electronics that geometrically conform to rigid 3D surfaces. Such conformal electronics are critical for emerging automotive, aerospace, robotics, biomedical, energy, and environmental applications. Compared to existing manufacturing techniques Form-fuse can realize superior electrical performance for geometrically complex surfaces and access to a wider array of materials. This award supports fundamental research to understand key mechano-electrical phenomena in Form-Fuse. It can positively impact the production and performance of advanced electronics that are critical to the nation’s prosperity and security. This multi-institutional project involves several disciplines including manufacturing, modeling, machine learning, and design and will further broaden the participation and education of diverse underrepresented groups in manufacturing. A critical limitation of existing manufacturing techniques for conformal electronics is the inability to achieve high electrical performance for complex surface geometries without sacrificing size- and material-scalability. The Form-Fuse process involves printing of nanoparticles on flat polymer sheets, forming this assembly to match the shape of the targeted 3D surface, fusing the nanoparticles using light, and attaching this final assembly to the targeted 3D surface. Using multiple intermediate forming stages overcomes the above limitations of the state-of-the-art manufacturing methods. This research will address key knowledge gaps on the physical mechanisms that drive electrical performance in Form-Fuse. The research team will perform experiments to characterize the impact of the polymer’s thermomechanical history on electrical performance, establish physics-based models to reveal and predict the deformation mechanisms that drive electrical performance, and create physics-guided techniques for rational and scalable design of process parameters and intermediate stage geometries. These tasks will create the scientific foundation for understanding and scaling the Form-Fuse process in a cost-effective manner. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

Magnetohydrodynamic (MHD) turbulence, involving magnetic fields and fluid motion, is crucial to the movement of gas in galaxies and essential for understanding star and planet formation. However, the complexity of MHD turbulence has hindered the development of a comprehensive theory of star formation and stellar convection. This project leverages machine learning to enhance turbulence resolution in simulations. By integrating machine learning with traditional methods, the project aims to uncover fundamental equations of turbulent systems and develop new star formation models. Beyond astrophysics, this research advances education and societal engagement via a novel collaboration with the Gibney Dance Company to use dance in communicating scientific concepts. Additionally, the project enhances the Catalog for Astrophysical Turbulence Simulations (CATS) database, creating educational resources and tools for future research in MHD turbulence. The primary goals are to: Develop machine learning tools for super-resolution in MHD turbulence and Rayleigh-Bénard convection simulations. Test and refine analytic models of turbulent star formation and apply machine learning to derive symbolic prescriptions for star formation processes. Additionally, the PIs will enhance the CATS database, creating Python notebooks and educational materials for classroom and broader use. The improved CATS database will serve as a gold standard for training and testing machine learning models in MHD turbulence. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

This Engineering Emerging areas of Advanced Manufacturing (ENG-EAM) award supports research that will focus on establishing systemic and robust resilience to cyberphysical attacks on connected digital manufacturing systems. Digitization and connectivity are the cornerstones of modern manufacturing, but these very qualities allow cyberphysical attacks to negatively impact part performance by stealthily altering the digital representations of geometry, process plans, and/or in-situ sensing signals. This has the potential to pose a significant threat to societal well-being, economic stability, and national security by introducing defective parts into electronics, spacecraft, planes, automobiles, biomedical devices, and energy components. The state-of-the-art practice of dealing with such attacks by sacrificing productivity, yield, cost, and connectivity to ensure part performance critically limits pervasive and trustworthy adoption of Industry 4.0 and digital manufacturing. This research project will create and validate a novel computational paradigm called Smart-Recover that actively assures every part’s performance despite cyberphysical attacks and with minimal loss in productivity, yield, connectivity, or cost-effectiveness. The research will be complemented by developing a multi-institutional manufacturing cybersecurity education program for workforce development across high school, undergraduate, and graduate educational levels. The specific goal of the research is to establish the mathematical basis for the Smart-Recover paradigm, which combines pre-fabrication correction of attack-altered geometric models with stoppage-free in-process mitigation of defects created by attack-modified process plans and attack-distorted in-situ sensing signals. To this end, the research objectives include the creation of techniques for: (1) pre-fabrication computational reconstruction of only the attack-altered features of the digital geometric model; (2) in-process remodification of process plans to disrupt formation of local defects induced by atypical attack-driven alteration of exogenous process parameters; and (3) in-process restoration of defect prediction accuracy for attack-altered sensor signals at speeds necessary for local defect mitigation. The research team will further explore the generalizability and collective interaction of these elements of Smart-Recover with stealthy and system-spanning cyberphysical attacks via two manufacturing testbeds. These advances will be achieved via innovations at the convergence of geometric design, machine learning, in-situ sensing, and physics-based modeling. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

Compared to traditional manufacturing processes such as machining, laser powder bed fusion (LPBF)-based metal additive manufacturing (AM) offers an opportunity for making complex metal components with design freedom, short development time, and environmental sustainability. However, the LPBF fabricated components often suffer from severe fatigue scattering problems, that is, the fatigue life of a component produced by LPBF under similar process conditions exhibits a very large variation. Fatigue scattering imposes a significant challenge to using an LPBF process for fabricating load-bearing and highly reliable components. This significantly limits the applicability of metal AM processes. To address this limitation, the objective of this project is to establish a physics-informed machine learning (PIML) framework, which integrates the physical knowledge of fatigue and the measured data to enable accurate and transparent predictions of fatigue life and its variation. Based on the PIML framework, process design optimization can be achieved to mitigate the fatigue scattering. The new knowledge and modeling methods obtained from this project will bring disruptive impacts on the AM industry by providing an enabling predictive tool for the fatigue life and scattering of printed materials. The scattering mitigation strategy facilitates printing consistent high-quality components in batch or mass production. This project will also contribute to workforce training by promoting interdisciplinary research at the intersection of AM, fatigue mechanics, and machine learning and provide unique training opportunities and learning testbeds for students. To achieve the project objective, baseline fatigue samples as-printed via LPBF, and the post-processed (i.e., hot isotropic pressing) alloys, including SS316L and Ti-6Al-4V alloys will be fabricated. The sample quality including surface finish, geometrical defects, residual stress, and microstructure will be characterized. Then high-frequency and load-varying resonance-based fatigue testing (up to 20 million cycles/day) will be conducted to obtain the data of fatigue initiation, development, and fracture behaviors. With the experimentally obtained fatigue data, the PIML framework is established to integrate the governing phenomenological laws or physics-driven fatigue laws and uncertainty quantification with data-driven deep neural networks to enable the accurate, data-efficient, and interpretable prediction of fatigue life, scattering band, and dynamic fatigue behavior. A scattering mitigation strategy will be established as well using Bayesian optimization based on the established process-quality-fatigue (P-Q-F) model. If successful, this project can generate new understanding in the P-Q-F relationship of LPBF, which will advance the metal AM industry. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

NONTECHNICAL SUMMARY An improved understanding of the electronic properties of materials is fundamental to the development of many modern technologies, especially those relying on electronic, magnetic, and optical materials. In recent years, a class of mathematical methods based on so-called "Berry phases" and related topological concepts have begun having a profound impact on our understanding of the electronic structure of materials and on our ability to compute their properties. This research project is focused on the translation of these mathematical concepts into practical tools for the computation of important properties of materials, including materials whose electronic configuration shows unconventional topological behavior. Part of the project focuses on a better understanding of the underlying theory, together with the development of new or improved computer codes that embody these mathematical methods. Another part of the work involves the application of these mathematical and computational tools to improve our understanding of known materials and to assist in the search for new materials with improved properties. The research is expected to advance the availability of materials with useful electronic, magnetic, and optical properties. The project involves training and mentorship of graduate students that will contribute to their career advancement and to the development of scientific workforce, while algorithms and computer codes developed by the project will be made available in open-source form for the benefit of the wider scientific research community. The project also holds out promise for the identification and evaluation of electronic materials that may ultimately find commercial applications. TECHNICAL SUMMARY This project is focused on theoretical research on the electronic properties of materials, with a special emphasis on physical properties whose underlying mathematical description involves geometric quantities based on Berry phases and curvatures. These are typically properties for which macroscopic orbital currents play an important role, and include electric polarization, orbital magnetization, anomalous Hall conductivity, circular and gyrotropic optical effects, and the subtle role of magnetism on lattice dynamics. The proper mathematical description of these properties underlies much recent progress in the theory of topological insulator and semimetal phases and of moire-scale multilayer systems that have been the focus of recent attention. The objectives of this project are: i) to further develop the formal theory of the aspects of electronic structure that depend upon a description in terms of geometric quantities; ii) to invent and disseminate accurate and efficient computational methods for computing materials properties related to these mathematical concepts; and iii) to use computational methods to identify promising new materials or structures in which these properties can manifest themselves, potentially leading to technological applications. The need to understand the relations between bulk and surface properties requires further progress in our ability to describe geometrical properties locally, not just globally, motivating further progress in the theory of "local markers" and related approaches. It is timely to extend theories developed for static systems to include frequency dependence, especially regarding ordinary and spatial-dispersive optical effects that occur in low-symmetry (e.g., chiral magnetic) materials. The influence of electronic Berry phases and curvatures on the dynamics of phonons in spin-orbit coupled magnetic materials will also be explored. As a cross-cutting theme, first-principles calculations will be carried out to quantify the physical properties that are predicted on the basis of the newly developed formal descriptions, with an eye towards identifying novel materials showing unusual or enhanced properties. The project is expected to result in the development of algorithms that will ultimately be implemented in open-source code packages and made available to the wider electronic-structure community. Training and mentorship of the graduate students will contribute to scientific workforce development. The project also holds out promise for the identification and evaluation of electronic materials that may ultimately find commercial applications. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

The project aims to advance our understanding of quantum mechanics, a notoriously puzzling theory, and the ways it differs from classical physics. It focuses on the mathematics used to formulate the theory—in particular, complex numbers—aiming to understand why complex numbers are so central to quantum mechanics as opposed to classical physics. The project will result in several research articles and presentations, provide mentoring opportunities for women working in foundations of physics, and yield a piece on pedagogy for teachers of introductory quantum mechanics courses. The project will advance the public's understanding of quantum mechanics and the mathematics required for it by producing a piece aimed at a general scientific audience, outlining the main ideas of the project while critically evaluating recent claims that complex numbers are truly essential to describing quantum physics. The PI will develop a new undergraduate course on the mathematical and conceptual foundations of quantum mechanics. This project in the philosophical foundations of physics addresses a question of longstanding interest and importance, asked by just about every famous physicist who has thought about the theory since its inception (including the likes of Schrödinger, Pauli, and Bohm), yet which still lacks an agreed-upon answer: are complex numbers essential to quantum mechanics, and if so, why? In classical physics, complex numbers are used as a dispensable calculational tool; in quantum mechanics, they seem to be playing a newly integral role. What is about quantum mechanics and the kinds of phenomena it describes that makes complex numbers so central to the theory? The project will show that an especially elucidating answer flows from distinctively quantum phenomena involving spin. This yields direct insight into the nature of quantum mechanical systems and the mathematics used to describe them, and helps elucidate how quantum mechanics differs from classical physics. It further yields insight into the nature of mathematically formulated scientific theories more generally, particularly the relationship between the mathematics used to formulate a theory and the nature of the world itself. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

This Ecosystem for Leading Innovation in Plasma Science and Engineering (ECLIPSE) grant supports research that contributes new knowledge related to a novel manufacturing process called magnetically enhanced laser induced plasma (M-ELIP) micromachining, promoting the progress of science, and benefiting US industries. This manufacturing process uses plasma generated by a laser to create complex micro-components. Such micro-scale components are critical for semiconductor and optical electronics, biomedical implants, wearable devices, solar cells, and electromechanical aerospace and defense parts. Compared to existing techniques, the process planned in this project can increase the geometric resolution of such components by at least three-fold while increasing their production speed by two and a half times. This award supports fundamental research to understand laser-plasma-magnetic field-workpiece interactions. Given the above applications, the results from this research impact the production and operational functionality of components that are foundational to the Nation’s prosperity and security. This collaborative multi-institutional project involves several disciplines including manufacturing, modeling, sensing, and machine learning and broadens the participation and education of women and underrepresented minority students in manufacturing and engineering. Current state-of-the-art micromilling methods such as laser-induced plasma and direct laser ablation are limited in planar resolution and aspect ratio. The enhanced magnetically assisted laser induced plasma (E-MLIP) micromilling process involves focusing a pulsed laser inside a dielectric liquid to create a plasma at the focal spot, simultaneously shaping the plasma via an external magnetic field, and bringing the plasma in contact with the workpiece located inside the liquid to remove material. E-MLIP overcomes the limitations of planar resolution and aspect ratio of the machined features in laser-induced plasma (LIP) and direct laser ablation (DLA) micromilling. This research addresses the knowledge gaps on the physical mechanisms of material removal in E-MLIP. The research team will perform experiments to characterize material removal and defect formation, develop physics-based models to explain and predict the mechanisms that drive such material removal and flaw formation, and use a combination of in-situ acoustic-based sensing and electromagnetic finite element analysis to detect and correct defects during the process. Together, these tasks enable a physics- and sensor-data informed defect correction paradigm for understanding and scaling the magnetically enhanced laser induced plasma micromachining process in a cost-effective manner. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

With support from the Chemical Structure and Dynamics (CSD) program in the Division of Chemistry, Professor Claudio Margulis of the University of Iowa and Professor Andrew Nieuwkoop of Rutgers University are investigating the structure and dynamics of ionic liquids (ILs) and more specifically what they call their “liquid inside a liquid” behavior using a variety of experimental and computational techniques. A challenging aspect of the work is that it deals with an “in-between” dynamical regime in which ILs are too viscous for sophisticated liquid-state NMR techniques but too soft for solid state NMR; this challenge extends also to the computational realm as highly viscous ILs, such as those close to their glass transition, are difficult to simulate. The PIs will use a combination of advanced NMR techniques, scattering, and computer simulations, to jointly investigate intra- and inter-ionic dynamics in an atom-selective manner for a set of selected ILs. Collaboration with Prof. Sharon Lall-Ramnarine from Queensborough Community College and her students as well as other scientists will continue to broaden the reach of the work. Professors Margulis and Nieuwkoop will probe whether the charge network present in all ILs and the apolar domain present for some ILs, each slow down at a different rate; in other words, if the charge network (the matrix) “rigidifies” first and only at lower temperature do the secondary motions of the apolar domain slow down. The study of specific ILs across a range of temperatures could also shed light on structural population changes in the condensed phase. Their discoveries could impact our understanding of fundamental phenomena such as how ions move on time scales relevant to transport phenomena; they could also shed light on recently discovered, but not yet understood, liquid-liquid phase transitions in ILs. Finally, Professors Margulis and Nieuwkoop will continue to engage students from community college, graduate students, and postdocs as well as colleagues at symposia and conferences where vibrant scientific exchanges on ILs will take place. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

The Research Internships in Ocean Science (RIOS) program will bring ten undergraduates to Rutgers each summer for three years. Participating undergraduates will be immersed in a ten-week summer research experience that includes professional development activities and career mentoring. RIOS interns work directly with internationally recognized faculty mentors, in high caliber facilities, at multiple coastal sites. Students will conduct research at one of two locations; the New Brunswick main campus or the Haskin Shellfish Research Lab, a field station located on the NJ coast. Through independent projects and team research experiences, students focus on process-oriented concepts applicable in marine systems. Independent research projects are embedded within ongoing programs, focused on the continental shelf, adjacent estuaries, polar regions, and advanced ocean observing. Students write research proposals, participate in cruises, conduct laboratory and field research, analyze data, and communicate their results throughout the program. A poster designed for display at a national scientific meeting communicates their hypotheses tested, approach taken, and knowledge gained. The RIOS program is designed to support outcomes that go beyond the research that students and mentors advance year after year. RIOS leverages highly successful faculty research programs and departmental research assets, college and undergraduate networks, and proven mentoring support frameworks to provide a meaningful and transformative research experience for its participants. The program attracts undergraduates that would otherwise have limited access to hands-on oceanographic research opportunities. The program design helps participants engage with, and be retained in, science-related fields, and bolsters networks and partnerships between academics, government agencies, private companies, and other places that the RIOS alumni progress to in their future paths. The research topics undertaken by RIOS interns are varied and cutting edge. Students can select projects that utilize ocean observing system data collected using technologies such as ocean gliders, satellites, coastal-ocean dynamics applications radar, or underwater video. More field-focused projects could include hands-on research in local bays, estuaries, and on the continental shelf off New Jersey. Laboratory based projects have access to molecular facilities for studying marine viruses, bacteria, or other microorganisms, as well as organic or inorganic analyses for the purpose of paleoceanographic or eutrophication studies. During their internship, guided by their faculty mentors, RIOS interns craft rigorous research projects of scientific and societal importance. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

This project aims to develop (artificial intelligence) AI-powered approaches to address challenging societal problems, such as dealing with droughts, infectious diseases, and environmentally harmful emissions. This project engages specific questions in these areas, such as: How can one effectively allocate water resources to increase agricultural drought resilience during drought seasons? How can one effectively determine when and where to construct hydrogen or electric vehicle refueling stations to encourage citizens to adopt these technologies to lower emissions? How can one effectively distribute vaccines and other medical supplies daily to enhance response to infectious diseases during pandemics? These problems belong to a class of classical and important problems in sequential collective decision-making. While sequential decision-making and collective decision-making have been studied previously, decision-making problems that are simultaneously sequential and collective are poorly understood, especially for specific domains such as resource allocation and when combined with goals such as responsibility and equitability. The overarching project goal is to establish theoretical and algorithmic foundations for responsible and equitable AI-powered sequential, collective decision-making. It also seeks to ensure that sequences of decisions satisfy multiple objectives and make appropriate trade-offs between short and long-term rewards subject to fairness criteria. The proposed research will lead to efficient and fair solutions to our social good and use-inspired applications in drought resilience, towards net zero, and infectious disease resilience. The project will achieve its goals by focusing on three interconnected challenges, leveraging a wide range of techniques from AI, economics, and operation research. First, the challenge of fair multi-objective collective decision-making for a single time period subject to multiple objectives and fairness criteria. Second, explore fair sequential multi-objective collective decision-making that addresses trade-offs between immediate and long-term efficiency and fairness. Finally, understand the strategic aspects of fair multi-objective collective decision-making in collaboration with stakeholders, who provide information that facilitates the process. This is a joint project between United States and Australian researchers funded by the Collaboration Opportunities in Responsible and Equitable AI under the U.S. NSF and the Australian Commonwealth Scientific and Industrial Research Organisation (CSIRO). This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

Artificial Intelligence (AI) has reached groundbreaking milestones in recent years. Its usage has spanned critical application domains, such as computer vision, audio perception, and natural language processing. However, these breakthroughs come with substantial security challenges. The machine learning (ML) models serving as the computational cores of AI systems are inherently vulnerable to attacks. By exploiting vulnerabilities in AI systems, adversaries can make the models produce incorrect predictions, leading to serious consequences such as misinterpreting traffic signs for autonomous vehicles or generating incorrect responses in speech recognition systems. Current AI-related educational efforts are limited on teaching the security perspective of ML. To bridge this gap, this project aims to develop comprehensive educational modules to prepare students and future engineers to address these ML security vulnerabilities and achieve trustworthy AI. By creating a practice-in-the-loop learning experience, students can obtain hands-on experiences with the security vulnerabilities of ML models and corresponding solutions. This project will develop a comprehensive educational program that focuses on three key perspectives of AI security. First, this project will create a practice-in-the-loop learning experience for students to understand the security of ML in computer vision, such as image recognition and object detection. Educational modules will be developed to cover various ML models for vision sensing and their security vulnerabilities and solutions. Second, this project will extend the interactive learning experience for students to understand the security problems of ML in voice assistant systems, such as speech recognition and speaker identification. The educational modules will be developed to introduce ML models for audio data processing and security vulnerabilities in voice assistant AI systems. Third, this project will develop software-based labs and training projects to enhance students’ understanding. The outcomes of this project, such as teaching slides, software labs, and training projects, will enable various undergraduate student training and outreach activities. They will also be disseminated online and through academic publications, ensuring diverse communities can readily access and employ the educational resources. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

The Florida Current represents the origins of the Gulf Stream that flows northward into the high latitude North Atlantic, eventually becoming the North Atlantic Current. The flow of this water mass warms the atmosphere above it, redistributing heat from the tropics to higher latitudes. As the North Atlantic Current cools near the Arctic, it becomes dense and sinks to the deep ocean then flows southward and in part drives the large-scale Atlantic meridional overturning circulation, which moderates climate in the Northern Hemisphere and beyond. Much attention has been focused on the possible slowdown of this circulation. Although researchers have some understanding of how the Florida Current has behaved in modern times, based on direct measurements, its past behavior is poorly constrained. Further, observations of the Florida Current are limited to recent decades and it has been suggested that the Florida Current has weakened over the last 40 years due to human caused climate change. However, longer-term (multidecadal to centennial) annual resolution data on the Florida Current are currently too scarce to confirm this. Because the period of direct instrumental observation is relatively short, to understand the natural variability of the system, the Florida Current must be studied by natural climate archives and proxy records. This investigation will utilize previously collected corals that are strategically located to address the questions: 1) What is the natural variability in annual changes to the Florida Current over the past several centuries? 2) How do those changes relate to climate drivers in the region, including decadal scale trends to regional and local conditions? The broader impact activities of this proposal include support for several underrepresented researchers, training and mentoring of undergraduate and graduate students, and two postdoctoral researchers. The investigators will create a museum display in the Hall of Planet Earth at the American Museum of Natural History. Direct observations of the components of the Atlantic Meridional Overturning Circulation (AMOC) are limited to the last couple of decades. Measurements of the Florida Current, a critical component of AMOC, have only existed for the last 40 years with recent work suggesting a modest decline in the Florida Current over the past century; however, annual resolution and long-term (> 50 to 100 years) data on the Florida Current and AMOC are necessary to further evaluate this. The proposed research will contribute much needed information on the rates and processes of the Florida Current over the past 200-300 years. Using previously collected Siderastrea and Colpophyllia corals from Tobago and the Florida Straits that faithfully record oceanographic and climate signals within the geochemistry of their skeletons, this work will reconstruct Florida Current flow from water mass properties. Annual-resolution measurements of radiocarbon content will resolve water mass source variations between several inflow routes into and through the Caribbean Sea that contribute to the velocity of the Florida Current. Sea surface temperature (SST) and salinity (SSS) obtained at monthly resolution from ratios of strontium to calcium (Sr/Ca) and oxygen isotopes (δ18O) respectively will add higher resolution information on water mass properties due to specific hydroclimates in water source regions. The corals are strategically located: one is in the heart of the Florida Current, the second is at the southern end-member of the Caribbean in-flow. Together, these sites will “close the loop” on interactions between the Florida Current and the relative contribution of northern and southern water to the Caribbean current. By integrating data from several locations, this work will better constrain multidecadal variability in the rate of the Florida Current flow before the onset of anthropogenic changes. Box models utilizing the water mass signals of radiocarbon, SST and SSS will be combined with reanalysis of regional Lagrangian output Ocean General Circulation Models to reconstruct decadal resolved changes to the Florida Current source water over the last several centuries. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.